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--- |
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library_name: transformers |
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language: |
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- multilingual |
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- bn |
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- cs |
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- de |
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- en |
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- et |
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- fi |
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- fr |
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- gu |
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- ha |
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- hi |
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- is |
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- ja |
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- kk |
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- km |
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- lt |
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- lv |
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- pl |
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- ps |
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- ru |
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- ta |
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- tr |
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- uk |
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- xh |
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- zh |
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- zu |
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license: apache-2.0 |
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base_model: answerdotai/ModernBERT-base |
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tags: |
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- quality-estimation |
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- regression |
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- generated_from_trainer |
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datasets: |
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- ymoslem/tokenized-wmt-da-human-evaluation |
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model-index: |
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- name: Quality Estimation for Machine Translation |
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results: |
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- task: |
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type: regression |
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dataset: |
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name: ymoslem/wmt-da-human-evaluation-long-context |
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type: QE |
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metrics: |
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- name: Pearson |
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type: Pearson Correlation |
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value: 0.4465 |
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- name: MAE |
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type: Mean Absolute Error |
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value: 0.126 |
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- name: RMSE |
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type: Root Mean Squared Error |
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value: 0.1623 |
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- name: R-R2 |
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type: R-Squared |
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value: 0.0801 |
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- task: |
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type: regression |
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dataset: |
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name: ymoslem/wmt-da-human-evaluation |
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type: QE |
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metrics: |
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- name: Pearson |
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type: Pearson Correlation |
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value: |
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- name: MAE |
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type: Mean Absolute Error |
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value: |
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- name: RMSE |
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type: Root Mean Squared Error |
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value: |
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- name: R-R2 |
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type: R-Squared |
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value: |
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metrics: |
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- pearsonr |
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- mae |
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- r_squared |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# Quality Estimation for Machine Translation |
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This model is a fine-tuned version of [answerdotai/ModernBERT-base](https://huggingface.co/answerdotai/ModernBERT-base) on the ymoslem/tokenized-wmt-da-human-evaluation dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0571 |
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## Model description |
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This model is for reference-free, sentence level quality estimation (QE) of machine translation (MT) systems. |
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The long-context / document-level model can be found at: [ModernBERT-base-long-context-qe-v1](https://huggingface.co/ymoslem/ModernBERT-base-long-context-qe-v1), |
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which is trained on a long-context / document-level QE dataset [ymoslem/wmt-da-human-evaluation-long-context](https://huggingface.co/datasets/ymoslem/wmt-da-human-evaluation-long-context) |
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## Training and evaluation data |
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This model is trained on the sentence-level quality estimation dataset: [ymoslem/wmt-da-human-evaluation](https://huggingface.co/datasets/ymoslem/wmt-da-human-evaluation) |
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## Training procedure |
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This version of the model uses the full lengthtokenizer.model_max_length=8192, |
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but it is still trained on a sentence-level QE dataset [ymoslem/wmt-da-human-evaluation](https://huggingface.co/datasets/ymoslem/wmt-da-human-evaluation) |
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The long-context / document-level model can be found at: [ModernBERT-base-long-context-qe-v1](https://huggingface.co/ymoslem/ModernBERT-base-long-context-qe-v1), |
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which is trained on a long-context / document-level QE dataset [ymoslem/wmt-da-human-evaluation-long-context](https://huggingface.co/datasets/ymoslem/wmt-da-human-evaluation-long-context) |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 0.0003 |
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- train_batch_size: 128 |
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- eval_batch_size: 128 |
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- seed: 42 |
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- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
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- lr_scheduler_type: linear |
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- training_steps: 10000 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | |
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|:-------------:|:------:|:-----:|:---------------:| |
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| 0.0686 | 0.1004 | 1000 | 0.0712 | |
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| 0.0652 | 0.2007 | 2000 | 0.0687 | |
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| 0.0648 | 0.3011 | 3000 | 0.0623 | |
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| 0.0609 | 0.4015 | 4000 | 0.0600 | |
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| 0.0585 | 0.5019 | 5000 | 0.0603 | |
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| 0.0588 | 0.6022 | 6000 | 0.0589 | |
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| 0.0592 | 0.7026 | 7000 | 0.0581 | |
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| 0.0585 | 0.8030 | 8000 | 0.0574 | |
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| 0.0588 | 0.9033 | 9000 | 0.0572 | |
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| 0.0563 | 1.0037 | 10000 | 0.0571 | |
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### Framework versions |
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- Transformers 4.48.1 |
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- Pytorch 2.4.1+cu124 |
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- Datasets 3.2.0 |
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- Tokenizers 0.21.0 |
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